WHICH DEVELOPMENT INDICATORS ARE IMPORTANT IN ADDRESSING FOOD SECURITY ACROSS THE WORLD?

Group - Food Security by Oumayma, Sumin, Taeho, and Younghwan

Introduction

According to the 1996 World Food Summit, food security is defined as “the state in which people at all times have physical, social and economic access to sufficient and nutritious food that meets their dietary needs for a healthy and active life.” The importance of food security is escalating rapidly more than ever amidst the issue of climate change, agricultural production and the COVID-19 pandemic. World Food Programme (WFP), recipient of the Nobel Peace Prize of 2020, warned the international community that “the huge risk of food security will be synchronized after COVID19 pandemic.” With this increasing importance in mind, our project aims to explore the relationship between political and economic development indicators and food security to eventually find out the significance of political and economic development and their impact on food security.

What is the ‘Global Food Security Index (GFS Index)’?

Global Food Security Index (GFS) is a dynamic quantitative and qualitative benchmarking model designed and constructed by the Economist Intelligence Unit with the sponsorship of Corteva AgriscienceTM. The index, which is published annually since 2012, measures the extent of food security of 113 developed and developing countries in four different aspects: food affordability, availability, quality and safety and natural resources and resilience.

merged_map <- left_join(world_map, Final_Project_in_IDS, by= "iso3c")
map_animated <- merged_map %>% 
    ggplot(aes(x= long, y= lat, group = group, fill = GFS,
               frame = year,
               text = country)) +
            geom_polygon() +
            theme_void()+
            scale_fill_viridis_c()
ggplotly(map_animated) %>%
    animation_opts(
        1000, easing = "elastic", redraw = FALSE
    )

The map above shows the yearly change in the countries’ GFS Index from the start of generation of the latter in 2012 until 2019. The countries’ performance on the Index ranges from 36.6 as the lowest scored by Sudan to 85.1 as the highest value scored by Finland. The trend over the years seems positive for almost all countries. However, several countries still do not reach half of the index score while some others are not included in the report.

The relationships between food security and income levels tend to be stronger for countries in North America and Europe compared to Africa and South America. In addition, countries in Asia seem to have bigger changes. This can be due to their economic development and better agricultural condition. Government strategy could also be a big contributor to this trend, especially for China and countries in the Middle East.

Key Findings from Global Food Security Index in 2019:

1. There has been an improved access to food market data and mobile banking in 63 out of 113 countries.
2. While more than half of the countries have reduced poverty since the first edition of the index, some countries, particularly those in Asia, have made remarkable progress.
3. Highly volatile agriculture production in countries like Australia, Norway and Sweden demonstrates the risks that climate change poses to agriculture and food production.
4. High-income yet resource-constrained countries like Singapore, Bahrain, Qatar, the UAE and Kuwait face the highest rates of food import dependency.
5. Many countries in Africa and Asia face increasing risks from flooding, which can cause crop loss and lead to volatile food supplies.

Relationships between Global Food Security Index (GFS) and Development Indicators

1) Human Development Index (HDI)

Human Development Index (HDI), developed by the United Nations Development Programme (UNDP), is a representative development indicator based on three dimensions: long and healthy life, knowledge, and a decent standard of living. Thus, the indicator is composed of ‘life expectancy index’, ‘education index’, and ‘GNI per capita (PPP $)’ index.

ggplotly(Final_Project_in_IDS %>% 
            ggplot(aes(x = HDI,
                       y = GFS,
                       colour = continent,
                       frame = year)) +
            geom_point(aes(text = country,
                           size = Population)) +
             xlab('Human Development Index') +
             ylab('Global Food Security') + 
            theme(legend.position = "none"))

## Warning: Ignoring unknown aesthetics: text

There is a strong positive relationship between GFS and HDI. Countries with a high level of GFS tend to score higher on HDI, while countries with a low level of GFS tend to score lower on HDI. The annual trend from 2012 to 2019 has no big changes except that the Asian countries are moving towards right comparatively to those from other continents. Well developed policies for human development might contribute to improvement of GFS.

2) GDP per Capita

GDP per Capita, which divides a country’s Gross Domestic Product (GDP) by its size of population, is a classic indicator of countries’ status of economic growth.

ggplotly(Final_Project_in_IDS %>% 
            ggplot(aes(x = `GDP per Capita`,
                       y = GFS,
                       colour = continent,
                       frame = year)) +
            geom_point(aes(text = country,
                           size = Population)) +
             xlab('GDP per Capita') +
             ylab('Global Food Security') + 
            theme(legend.position = "none"))

## Warning: Ignoring unknown aesthetics: text

The coefficient between GFS and GDP per Capita is relatively positive. However, the differences by GDP per Capita range from around $4,000 to $20,000, indicating that the gap in countries’ GDP per Capita is not as big based on the range. The annual changes from 2012 to 2019) show that the range is getting wider. Therefore, the graph shows that first of all, a minimum level of economic development is needed to improve food security. For those with higher income levels ($20,0000 and above ), it would depend on the policy strategy of each country. Moreover, almost all countries are struggling to improve GFS, yet there are big burdens for countries at a bottom level.

3) Government Efficiency

The government effectiveness index, elaborated by the World Bank Group, measures the quality of public services, civil service, policy formulation, policy implementation and credibility of the government’s commitment to maintain or improve their qualities.

ggplotly(Final_Project_in_IDS %>% 
            ggplot(aes(x = `Government Efficiency`,
                       y = GFS,
                       colour = continent,
                       frame = year)) +
            geom_point(aes(text = country,
                           size = Population)) +
             xlab('Government Efficiency') +
             ylab('Global Food Security') + 
            theme(legend.position = "none"))

## Warning: Ignoring unknown aesthetics: text

Government efficiency has a positive relationship with GFS. The relationship between the two variables is very similar to those between HDI and GFS. However, the difference is that all countries show gradual improvement annually despite small changes in the gaps among countries. This suggests that government efficiency should be an essential condition to improve GFS, but less effective of a factor compared to HDI or GDP per Capita.

Resources Global Food Security : (https://foodsecurityindex.eiu.com/) Human Development Index : (http://hdr.undp.org/en/data) GDP per capita : (https://databank.worldbank.org/indicator/NY.GDP.PCAP.CD/1ff4a498/Popular-Indicators) Government Efficiency : (https://databank.worldbank.org/reports.aspx?source=1181&series=GE.EST)

# UI part using Shiny Dashboard
ui <- dashboardPage(skin = "blue",
                    dashboardHeader(title = "FOOD SECURITY"),
                    dashboardSidebar(
                        sidebarMenu(
                            menuItem("Dashboard", tabName = "dashboard", icon = icon("dashboard")),
                            menuItem(sliderInput("Map_change_of_years",
                                                 "Select year for C. Map",
                                                 min = min(Final_Project_in_IDS$year, na.rm = TRUE),
                                                 max = max(Final_Project_in_IDS$year, na.rm = TRUE),
                                                 value = min(Final_Project_in_IDS$year, na.rm = TRUE),
                                                 sep="", # Hide thousands , separator
                                                 step=1)),
                            menuItem(sliderInput("year",
                                                 "Select year for D. Graph",
                                                 min = min(Final_Project_in_IDS$year),
                                                 max = max(Final_Project_in_IDS$year),
                                                 value = min(Final_Project_in_IDS$year),
                                                 sep="", # Hide thousands , separator
                                                 step=1)),
                            menuItem(selectInput("variable",
                                                 "Select Variable for D. Graph:",
                                                 c("Human Development Index" = "HDI", 
                                                   "GDP per Capita" = "GDP per Capita",
                                                   "Government Efficiency" = "Government Efficiency"
                                                 ))))),
  
                    dashboardBody(
                        fluidRow(
                            infoBox(title = "OUMAYMA, TAEHO, SU MIN, YOUNGHWAN",
                                    value = "WHICH DEVELOPMENT INDICATORS ARE IMPORTANT IN ADDRESSING FOOD SECURITY ACROSS THE WORLD?", 
                                    subtitle = "Global Food Security Index(GFS) is an index that assesses the level of food affordability, availability, quality and safety, and natural resources and resilience across a set of 113 countries since 2012.",
                                    icon = icon("globe"),
                                    color = "aqua",
                                    width = 12)),
                        fluidRow(
                            box(title = "A. Top 5 countries in GFS Index (2019)",
                                width = 6, 
                                solidHeader = TRUE,
                                collapsible = TRUE,
                                status = "info",
                                plotlyOutput("top")),
                            box(title = "B. Bottom 5 countries in GFS Index (2019)",
                                width = 6, 
                                solidHeader = TRUE,
                                collapsible = TRUE,
                                status = "info",
                                plotlyOutput("bottom")),
                            fluidRow(
                                box(title = "C. Map: Global Food Security Index across the World",
                                    width = 12, 
                                    solidHeader = TRUE,
                                    collapsible = TRUE,
                                    status = "success",
                                    plotlyOutput("my_map"), 
                                    "Source: World Bank")),
                            fluidRow(
                                box(title = "D. Graph: Global Food Security Index and Development Indicators",
                                    width = 12, 
                                    solidHeader = TRUE,
                                    collapsible = TRUE,
                                    status = "info",
                                    plotlyOutput("ourPlot"),
                                    "Source: World Bank, UNDP, Global Food Security Index"))
                        )))

# {Server logic required to draw Graphs & Histogram}

server <- function(input, output) {
    
    output$my_map <- renderPlotly({
        temp <- Final_Project_in_IDS %>% 
            filter(year %in% input$Map_change_of_years)
        leftjoin_data <- left_join(world_map, temp, by= "iso3c")
        ggplot(leftjoin_data, aes(x= long, y= lat, group = group, fill=GFS, text = country))+
            geom_polygon() +
            theme_void()+
            scale_fill_viridis_c()
    })
    
    output$ourPlot <- renderPlotly({
        ani <- Final_Project_in_IDS %>% 
            filter(year == input$year) %>%
            ggplot(aes(x = !!as.name(input$variable),
                y = GFS,
                colour = continent,
                frame = year)) +
            geom_point(aes(#x = !!as.name(input$variable),
                           #y = GFS,
                           #colour = continent,
                           text = country,
                           size = Population),
                           show.legend = FALSE)
    })
    output$top <- renderPlotly({
        top_5 %>% 
            ggplot() +
            geom_col(aes(x= GFS, y= reorder(country, GFS), fill = GFS, width = 0.4)) +
            theme(axis.title.x = element_blank(), axis.title.y = element_blank()) +
            theme(legend.position = "none") +
            xlim(0,100)
    })
    
    output$bottom <- renderPlotly({
        bottom_5 %>% 
            ggplot() +
            geom_col(aes(x= GFS, y= reorder(country,- GFS), fill = GFS, width = 0.4)) +
            theme(axis.title.x = element_blank(), axis.title.y = element_blank()) +
            theme(legend.position = "none") +
            xlim(0,100)
    })
}

# Run the application 
shinyApp(ui = ui, server = server)